Search Results for author:
Found 9 papers, 0 papers with code
Comparison of Clustering Algorithms for Statistical Features of Vibration Data Sets
In this work, we present an extensive comparison of the clustering algorithms K-means clustering, OPTICS, and Gaussian mixture model clustering (GMM) applied to statistical features extracted from the time and frequency domains of vibration data sets.
Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications
Data-driven machine learning is playing a crucial role in the advancements of Industry 4. 0, specifically in enhancing predictive maintenance and quality inspection.
Autoencoder based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems
In this work, we present an autoencoder based end-to-end workflow for anomaly detection suitable for multivariate time series data in large industrial cooling systems, including explained fault localization and root cause analysis based on expert knowledge.
Smart Active Sampling to enhance Quality Assurance Efficiency
We propose a new sampling strategy, called smart active sapling, for quality inspections outside the production line.
Evaluation of Hyperparameter-Optimization Approaches in an Industrial Federated Learning System
In this work, we investigated the impact of different hyperparameter optimization approaches in an FL system.
Minimal-Configuration Anomaly Detection for IIoT Sensors
The increasing deployment of low-cost IoT sensor platforms in industry boosts the demand for anomaly detection solutions that fulfill two key requirements: minimal configuration effort and easy transferability across equipment.
Towards Robust and Transferable IIoT Sensor based Anomaly Classification using Artificial Intelligence
Further, we investigate the model performance on different pumps from the same type compared to those from the training data.
Industrial Federated Learning -- Requirements and System Design
Federated Learning (FL) is a very promising approach for improving decentralized Machine Learning (ML) models by exchanging knowledge between participating clients without revealing private data.
Combining Heterogeneously Labeled Datasets For Training Segmentation Networks
In this work we propose a cost function which allows integration of multiple datasets with heterogeneous label subsets into a joint training.
